Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach by Mohammed Aly, Mohamed H. Behiry

“…Abstract Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. …”
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Research on the Macrocell Wireless Channel Model Based on Physic-Inspired Support Vector Regression Algorithm Wireless Channel Model in Macrocell Environment by Qi Yao, Zhongyu Liu, Lixin Guo, Jiang Guo, Zuoyong Nan, Wei Liu, Jiangting Li

“…The method embeds the building transmission model (BTM) into a machine learning framework based on support vector regression. …”
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User-Centric Federated Learning: Trading off Wireless Resources for Personalization by Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert, Qianrui Li

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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

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Advancing mango quality assurance: Non‐destructive detection of spongy tissue using visible near‐infrared spectroscopy and machine learning classification by Patil Rajvardhan Kiran, Md Yeasin, Pramod Aradwad, T. V. Arunkumar, Roaf Ahmad Parray

“…Various machine learning models used notably, linear discriminant analysis, support vector machine, and logistic regression exhibited strong discriminative capabilities with higher accuracy reaching 99%. …”
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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

“…Conclusion: Machine learning models provide valuable insights into the determinants of under-five mortality, with the logistic regression model demonstrating superior predictive performance. …”
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Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate ca... by Zhiping Li, Liqin Yang, Ximing Wang, Huijing Xu, Wen Chen, Shuchao Kang, Yasheng Huang, Chang Shu, Feng Cui, Yongsheng Zhang

“…Four machine learning classifiers logistic regression (LR), random forest (RF), extra trees (ET), and multilayer perceptron (MLP) were employed for model training and evaluation to select the optimal model. …”
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Estimation of Soil Organic Carbon Content of Grassland in West Songnen Plain Using Machine Learning Algorithms and Sentinel-1/2 Data by Haoming Li, Jingyao Xia, Yadi Yang, Yansu Bo, Xiaoyan Li

“…Nine experiments were conducted under three variable scenarios to select the optimal model. We used this optimal model to achieve high-precision predictions of SOC content. …”
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From Narratives to Diagnosis: A Machine Learning Framework for Classifying Sleep Disorders in Aging Populations: The <i>sleepCare</i> Platform by Christos A. Frantzidis

“…<b>Results</b>: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of <b>81%</b> on the test set. …”
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Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study by Shasha Xue, Li Li, Zhuolun Liu, Feng Lyu, Fan Wu, Panxiao Shi, Yongmin Zhang, Lina Zhang, Zhaoxin Qian

“…Prediction models were developed using logistic regression, support vector machine (SVM), random forest, light gradient boosting machine (LightGBM), and explainable boosting machine (EBM). …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…The DML models learn the primary features using machine learning and deep learning models. …”
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Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life’s essential 8: a cross-sectional study by Haolin Shi, Yangyi Fang, Xiuhua Ma

“…Through comparison of the Area Under the Curve (AUC), Accuracy, F1-Score, Precision, Recall, Specificity, Receiver Operating Characteristic (ROC), Decision Curve Analysis (DCA), and Calibration Curve Analysis (CCA), the optimal performance achieved by the Light Gradient Boosting Machine (LightGBM) model incorporating the 20 features. …”
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Redefining Trauma Triage for Elderly Adults: Development of Age-Specific Guidelines for Improved Patient Outcomes Based on a Machine-Learning Algorithm by Ji Yeon Lim, Yongho Jee, Seong Gyu Choi, Yoon Hee Choi, Sam S. Torbati, Carl T. Berdahl, Sun Hwa Lee

“…Physiological indicators (e.g., systolic blood pressure; saturation of partial pressure oxygen; and alert, verbal, pain, unresponsiveness scale) were incorporated. Bayesian optimization was used to fine-tuned models for sensitivity and specificity, emphasizing the F2 score to minimize undertriage. …”
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Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“… This study develops a hierarchical machine learning framework to address the challenges of multi-crop yield prediction in semi-arid regions, focusing on sparse climate data, model interpretability, and heterogeneous climate-crop interactions. …”
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Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) by F. Jannati, F. Sarmadian

“…Moreover, the random forest machine learning model was utilized for spatial modeling, zoning mapping, and determining the significance of environmental variables in the land suitability evaluation process. …”
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Machine learning-based identification and assessment of snow disaster risks using multi-source data: Insights from Fukui prefecture, Japan by Zhenyu Yang, Hideomi Gokon, Qing Yu

“…We employed four machine learning models—Decision Tree, Random Forest, Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost)—to capture complex nonlinear relationships among influencing factors and applied SHAP (SHapley Additive exPlanations) theory to interpret variable contributions. …”
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A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data by Mathew Nicho, Ahmed Hamed, Tarek Gaber, Jamal Hamad Al Arimi

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To the issue of optimising the performance of a scroll compressor as part of a CO2 booster refrigerating machine in order to increase its efficiency by V. A. Pronin, A. V. Kovanov, V. A. Tsvetkov, E. N. Mikhailova, P. A. Belov

“…Development of conceptual model, allows to identify influence of various factors on scroll compressor operation and to build adequate mathematical model, to choose or develop necessary calculation methods.…”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…Grid search analysis was conducted to determine the optimal hyperparameter configuration for each model, employing a 5-fold cross-validation approach. …”
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A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection by Guanqing Kong, Guanqing Kong, Shuang Ma, Shuang Ma, Wei Zhao, Wei Zhao, Haifeng Wang, Haifeng Wang, Qingxi Fu, Qingxi Fu, Jiuru Wang

“…Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF) and XGBoost classifiers are used to construct epileptic seizure detection models based on the optimized detection features.ResultAccording to experimental results, the proposed method achieves 99.32% accuracy, 99.64% specificity, 99.29% sensitivity, and 99.32% score, respectively.ConclusionThe detection performance of the three classifiers is compared using 10-fold cross-validation. …”
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